14,619 research outputs found
Ising model for distribution networks
An elementary Ising spin model is proposed for demonstrating cascading
failures (break-downs, blackouts, collapses, avalanches, ...) that can occur in
realistic networks for distribution and delivery by suppliers to consumers. A
ferromagnetic Hamiltonian with quenched random fields results from policies
that maximize the gap between demand and delivery. Such policies can arise in a
competitive market where firms artificially create new demand, or in a solidary
environment where too high a demand cannot reasonably be met. Network failure
in the context of a policy of solidarity is possible when an initially active
state becomes metastable and decays to a stable inactive state. We explore the
characteristics of the demand and delivery, as well as the topological
properties, which make the distribution network susceptible of failure. An
effective temperature is defined, which governs the strength of the activity
fluctuations which can induce a collapse. Numerical results, obtained by Monte
Carlo simulations of the model on (mainly) scale-free networks, are
supplemented with analytic mean-field approximations to the geometrical random
field fluctuations and the thermal spin fluctuations. The role of hubs versus
poorly connected nodes in initiating the breakdown of network activity is
illustrated and related to model parameters
Project scheduling under undertainty – survey and research potentials.
The vast majority of the research efforts in project scheduling assume complete information about the scheduling problem to be solved and a static deterministic environment within which the pre-computed baseline schedule will be executed. However, in the real world, project activities are subject to considerable uncertainty, that is gradually resolved during project execution. In this survey we review the fundamental approaches for scheduling under uncertainty: reactive scheduling, stochastic project scheduling, stochastic GERT network scheduling, fuzzy project scheduling, robust (proactive) scheduling and sensitivity analysis. We discuss the potentials of these approaches for scheduling projects under uncertainty.Management; Project management; Robustness; Scheduling; Stability;
PriPeARL: A Framework for Privacy-Preserving Analytics and Reporting at LinkedIn
Preserving privacy of users is a key requirement of web-scale analytics and
reporting applications, and has witnessed a renewed focus in light of recent
data breaches and new regulations such as GDPR. We focus on the problem of
computing robust, reliable analytics in a privacy-preserving manner, while
satisfying product requirements. We present PriPeARL, a framework for
privacy-preserving analytics and reporting, inspired by differential privacy.
We describe the overall design and architecture, and the key modeling
components, focusing on the unique challenges associated with privacy,
coverage, utility, and consistency. We perform an experimental study in the
context of ads analytics and reporting at LinkedIn, thereby demonstrating the
tradeoffs between privacy and utility needs, and the applicability of
privacy-preserving mechanisms to real-world data. We also highlight the lessons
learned from the production deployment of our system at LinkedIn.Comment: Conference information: ACM International Conference on Information
and Knowledge Management (CIKM 2018
Differential evolution to solve the lot size problem.
An Advanced Resource Planning model is presented to support optimal lot size decisions for performance improvement of a production system in terms of either delivery time or setup related costs. Based on a queueing network, a model is developed for a mix of multiple products following their own specific sequence of operations on one or more resources, while taking into account various sources of uncertainty, both in demand as well as in production characteristics. In addition, the model includes the impact of parallel servers and different time schedules in a multi-period planning setting. The corrupting influence of variabilities from rework and breakdown is explicitly modeled. As a major result, the differential evolution algorithm is able to find the optimal lead time as a function of the lot size. In this way, we add a conclusion on the debate on the convexity between lot size and lead time in a complex production environment. We show that differential evolution outperforms a steepest descent method in the search for the global optimal lot size. For problems of realistic size, we propose appropriate control parameters for the differential evolution in order to make its search process more efficient.Production planning; Lot sizing; Queueing networks; Differential evolution;
Analysis of the finite-source multiclass priority queue with an unreliable server and setup time
In this article, we study a queueing system serving multiple classes of customers. Each class has a finite-calling population. The customers are served according to the preemptive-resume priority policy. We assume general distributions for the service times. For each priority class, we derive the steady-state system size distributions at departure/arrival and arbitrary time epochs. We introduce the residual augmented process completion times conditioned on the number of customers in the system to obtain the system time distribution. We then extend the model by assuming that the server is subject to operation-independent failures upon which a repair process with random duration starts immediately. We also demonstrate how setup times, which may be required before resuming interrupted service or picking up a new customer, can be incorporated in the model
Synthesis and Stochastic Assessment of Cost-Optimal Schedules
We present a novel approach to synthesize good schedules for a class
of scheduling problems that is slightly more general than the
scheduling problem FJm,a|gpr,r_j,d_j|early/tardy. The idea is to prime
the schedule synthesizer with stochastic information more meaningful
than performance factors with the objective to minimize the expected
cost caused by storage or delay. The priming information is
obtained by stochastic simulation of the system environment. The generated
schedules are assessed again by simulation. The approach is
demonstrated by means of a non-trivial scheduling problem from
lacquer production. The experimental results show that our approach
achieves in all considered scenarios better results than the
extended processing times approach
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